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Current Challenges in Patent Information Retrieval pp 389–407Cite as

Future Patent Search

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Part of the book series: The Information Retrieval Series ((INRE,volume 29))

Abstract

In this chapter we make some prediction for patent search in about ten year’s time—in 2021. We base these predictions on both the contents of the earlier part of the book, and on some data and trends not well represented in the book (for one reason or another). We consider primarily incorporating knowledge of different sorts of patent search into the patent search process; utilising knowledge of the subject domain of the search into the patent search system; utilising multiple sources of data within the search system; the need to address the requirement to deal with multiple languages in patent search; and the need to provide effective visualisation of the results of patent searches. We conclude the real need is to find ways to support search independent of language or location.

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Notes

  1. 1.

    See: http://www.piug.org/vendors.php.

  2. 2.

    See: http://www.sigir.org/forum/S2000/Patent_report.pdf.

  3. 3.

    See: http://trec.nist.gov.

  4. 4.

    See: http://clef.iei.pi.cnr.it.

  5. 5.

    See: http://pair.ir-facility.org/.

  6. 6.

    It is unfortunate that in the patent search community the term “semantic search” has come to mean two quite different things: on the one hand techniques which rely on opaque semantics emergent from the data like Latent Semantic Analysis [28], Random Indexing [29] and various related techniques which are now quite widely used in patent search and on the other hand techniques which use additional, often completely or partially or completely hand crafted, resources reflecting human understanding of the texts or domains under consideration [30]. Here we mean the latter.

  7. 7.

    See: http://www.wipo.int/ipccat/ipc.html.

  8. 8.

    ISO/IEC 13250:1999.

  9. 9.

    See: http://www.pattools.com/index.html.

  10. 10.

    See: http://www.toolpat.com.

  11. 11.

    See: http://www.surfip.com/.

  12. 12.

    See: http://linkeddata.org/.

  13. 13.

    See: http://www.swsi.org/.

References

  1. Larkey LS (1999) A patent search and classification system. In: Proceedings of DL-99, 4th ACM conference on digital libraries, pp 179–187

    Chapter  Google Scholar 

  2. Stock M, Stock WG (2006) Intellectual property information: A comparative analysis of main information providers. J Am Soc Inf Sci Technol 57(13):1794–803

    Article  Google Scholar 

  3. Dou H, Leveillé S (2005) Patent analysis for competitive technical intelligence and innovative thinking. Data Sci J 4:209–237

    Article  Google Scholar 

  4. Hunt D, Nguyen L, Rodgers M (eds) (2007) Patent searching; tools and techniques. Wiley, Hoboken

    Google Scholar 

  5. Simmons E (2006) Patent databases and Gresham’s law. World Pat Inf 28(4):291–293

    Article  MathSciNet  Google Scholar 

  6. Fujita S (2007) Revisiting document length hypotheses: NTCIR-4 CLIR and patent experiment at Patolis. Working notes of the 4th NTCIR workshop meeting, pp 238–245

    Google Scholar 

  7. van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworths, London

    Google Scholar 

  8. Voorhees EM (1985) The cluster hypothesis revisited. In: Proceedings of the 1985 ACM SIGIR conference on research and development in information retrieval, pp 188–196

    Chapter  Google Scholar 

  9. Furnas GW, Landauer TK, Gomez LM, Dumais ST (1987) The vocabulary problem in human-system communication. Commun ACM 30(11):964–971

    Article  Google Scholar 

  10. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge. Chap 9

    MATH  Google Scholar 

  11. Wicenec B (2008) Searching the patent space. World Pat Inf 30(2):153–155

    Article  Google Scholar 

  12. Fujii A, Iwayama M, Kando N (2007) Introduction to the special issue on patent processing. Inf Process Manag 43(5):1149–1153

    Article  Google Scholar 

  13. Fletcher JM (1993) Quality and risk assessment in patent searching and analysis. In: Recent advances in chemical information; proceedings of the 1992 international chemical information conference, 19–21 October 1992, Annecy. Royal Society of Chemistry, Cambridge, pp 147–156

    Google Scholar 

  14. Azzopardi L, Vinay V (2007) Accessibility in information retrieval. In: Proceedings ECIR 2008, pp 482–489

    Google Scholar 

  15. Arenivar JD, Bachmann CE (2007) Adding value to search results at 3M. World Pat Inf 29:8–19

    Article  Google Scholar 

  16. Hassler V (2005) Electronic patent information: An overview and research issues. In: Proceedings 2005 symposium on applications and the internet workshops. SAINT2005:378–380

    Google Scholar 

  17. Fujii A, Iwayama M, Kando N (2007) Introduction to the special issue on patent processing. Inf Process Manag 43(5):1149–1153

    Article  Google Scholar 

  18. Egghe L, Rousseau R (1998) A theoretical study of recall and precision using a topological approach to information retrieval. Inf Process Manag 34(2–3):191–218

    Article  Google Scholar 

  19. Iwayama M (2006) Evaluating patent retrieval in the third NTCIR workshop. Inf Process Manag 42:207–221

    Article  Google Scholar 

  20. Fujita S (2007) Technology survey and invalidity search: A comparative study of different tasks for Japanese patent document retrieval. Inf Process Manag 43(5):1154–1172

    Article  Google Scholar 

  21. Nuyts A, Giroud G (2004) The new generation of search engines at the European Patent Office. In: Proceedings of the 2004 international chemical information conference, 17–20 October 2004, Annecy. Infornortics Ltd, Malmesbury, pp 47–56

    Google Scholar 

  22. Wanner L, Baeza-Yates R, Brugmann S, Codina J, Diallo B, Escorsa E, Giereth M, Kompatsiaris Y, Papadopoulos S, Pianta E, Piella G, Puhlmann I, Rao G, Rotard M, Schoester P, Serafini L, Zervaki V (2008) Towards content-oriented patent document processing. World Pat Inf 30(1):21–33. doi:10.1016/j.wpi.2007.03.008

    Article  Google Scholar 

  23. Corbett P, Copestake A (2008) Cascaded classifiers for confidence-based chemical named entity recognition. In: BioNLP 2008: Current trends in biomedical natural language processing, pp 54–62

    Chapter  Google Scholar 

  24. Sun B, Mitra P Giles CL (2008) Mining, indexing, and searching for textual chemical molecule information on the web. In: Proceeding of the 17th international conference on World Wide Web, pp 735–744. doi:10.1145/1367497.1367597

    Chapter  Google Scholar 

  25. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc 18th international conf on machine learning, pp 282–289

    Google Scholar 

  26. Uren V, Sabou M, Motta E, Fernandez M, Lopez V, Lei Y (2010) Reflections on five years of evaluating semantic search systems. Int J Metadata Semant Ontol 5(2):87–98

    Article  Google Scholar 

  27. Segura NA, Salvador-Sanchez, Garcia-Barriocanal E, Prieto M (2011) An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the gene ontology. Knowl-Based Syst 24(1):119–133. doi:10.1016/j.knosys.2010.07.012

    Article  Google Scholar 

  28. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

    Article  Google Scholar 

  29. Sahlgren M, Karlgren J (2005) Automatic bilingual lexicon acquisition using random indexing of parallel corpora. Nat Lang Eng 11(3):327–341. doi:10.1017/S1351324905003876

    Article  Google Scholar 

  30. Fernandez M, Lopez V, Sabou M, Uren V, Vallet D, Motta E, Castells P (2008) Semantic search meets the Web. In: Proceedings of the 2008 IEEE international conference on semantic computing (ICSC’08), pp 253–260. doi:10.1109/ICSC.2008.52

    Chapter  Google Scholar 

  31. Michel J, Bettels B (2001) Patent citation analysis; a closer look at the basic input data from patent search reports. Scientometrics 51(1):185–201

    Article  Google Scholar 

  32. Frackenpohl G (2002) PATCOM—the European commercial patent services group. World Pat Inf 24(3):225–227

    Article  Google Scholar 

  33. Ebersole JL (2003) Patent information dissemination by patent offices: striking the balance. World Pat Inf 25(1):5–10

    Article  Google Scholar 

  34. Höfer H, Siemens Business Services GmBH (2002) Method of categorizing a document into a document hierarchy. European Patent Application EP1244027-A1

    Google Scholar 

  35. Smith H (2002) Automation of patent classification. World Pat Inf 24(4):269–271

    Article  Google Scholar 

  36. Fall CJ, Törcsvári A, Benzineb K, Karetka G (2003) Automated categorization in the international patent classification. ACM SIGIR Forum 37(1):10–25. doi:10.1145/945546.945547

    Article  Google Scholar 

  37. Fall CJ, Torcsvari A, Fievet P, Karetka G (2004) Automated categorization of German-language patent documents. Expert Syst Appl 26(2):269–277

    Article  Google Scholar 

  38. Loh HT, He C, Shen L (2006) Automatic classification of patent documents for TRIZ users. World Pat Inf 28(1):6–13

    Article  Google Scholar 

  39. Li X, Chen H, Zhang Z, Li J (2007) Automatic patent classification using citation network information: an experimental study in nanotechnology. In: Proceedings of the 7th ACM/IEEE joint conference on digital libraries JCDL’07, pp 419–427. doi:10.1145/1255175.1255262

    Google Scholar 

  40. Kim JH, Choi KS (2007) Patent document categorization based on semantic structural information. Inf Process Manag 43(5):1200–1215

    Article  Google Scholar 

  41. Trappey AJC, Hsu FC, Trappey CV, Lin CI (2006) Development of a patent document classification and search platform using a back-propagation network. Expert Syst Appl 31(4):755–765

    Article  Google Scholar 

  42. Bel N, Koster CHA, Villegas M (2003) Cross-lingual text categorisation. In: Proceedings ECDL. LNCS, vol 2769, pp 126–139

    Google Scholar 

  43. Olsson JS, Oard DW, Hajic J (2005) Cross-language text classification. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp 645–646

    Chapter  Google Scholar 

  44. Rigutini L, Maggini M, Liu B (2005) An EM based training algorithm for cross-language text categorization. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence, pp 19–22

    Google Scholar 

  45. Huang SH, Ke HR, Yang WP (2008) Structure clustering for Chinese patent documents. Expert Syst Appl 34(4):2290–2297

    Article  Google Scholar 

  46. Ma Q, Nakao K, Enomoto K (2005) Single language information retrieval at NTCIR-5. In: Proceedings of NTCIR-5 workshop meeting, December 6–9, 2005, Tokyo, Japan

    Google Scholar 

  47. Suh JH, Park SC (2006) A new visualization method for patent map: Application to ubiquitous computing technology. LNAI, vol 4093, pp 566–573

    Google Scholar 

  48. Fischer G, Lalyre N (2006) Analysis and visualisation with host-based software—The features of STN®AnaVist™. World Pat Inf 28(4):312–318

    Article  Google Scholar 

  49. Blanchard A (2007) Understanding and customizing stopword lists for enhanced patent mapping. World Pat Inf 29(4):308–316

    Article  Google Scholar 

  50. Eldridge J (2006) Data visualisation tools—a perspective from the pharmaceutical industry. World Pat Inf 28(1):43–49

    Article  Google Scholar 

  51. Kim YG, Suh JH, Park SC (2008) Visualization of patent analysis for emerging technology. Expert Syst Appl 34(3):1804–1812

    Article  Google Scholar 

  52. Fattori M, Pedrazzi G, Turra R (2003) Text mining applied to patent mapping: a practical business case. World Pat Inf 25(4):335–342

    Article  Google Scholar 

  53. Lopes AA, Pinho R, Paulovich FV, Minghim R (2007) Visual text mining using association rules. Comput Graph 31(3):316–326

    Article  Google Scholar 

  54. Yang Y, Akers L, Klose T, Yang CB (2008) Text mining and visualization tools—Impressions of emerging capabilities. World Pat Inf 30(4):280–93

    Article  Google Scholar 

  55. Yang YY, Akers L, Yang CB, Klose T, Pavlek S (2010) Enhancing patent landscape analysis with visualization output. World Pat Inf 32(3):203–220. doi:10.1016/j.wpi.2009.12.006

    Article  Google Scholar 

  56. Harper DJ, Kelly D (2006) Contextual relevance feedback. In: Proceedings of the 1st international conference on information interaction in context, Copenhagen, Denmark, October 18–20, 2006. IIiX, vol 176. ACM, New York, pp 129–137. doi:10.1145/1164820.1164847

    Chapter  Google Scholar 

  57. Paulovich FV, Minghim R (2006) Text map explorer: a tool to create and explore document maps. In: Information visualization (IV06). IEEE Computer Society Press, London

    Google Scholar 

  58. Paulovich FV, Nomato LG, Minghim R, Levkowitz H (2006) Visual mapping of text collections through a fast high precision projection technique. In: Information visualization (IV06). IEEE Computer Society Press, London

    Google Scholar 

  59. Havukkala I (2010) Biodata mining and visualization: novel approaches. World Science Publishing Co, Singapore

    Google Scholar 

  60. Yu Z, Nakamura Y (2010) Smart meeting systems: A survey of state-of-the-art and open issues. ACM Comput Surv 42(2):1–20. doi:10.1145/1667062.1667065

    Article  Google Scholar 

  61. Nijholt A, Zwiers J, Peciva J (2009) Mixed reality participants in smart meeting rooms and smart home environments. Pers Ubiquitous Comput 13(1):85–94. doi:10.1007/s00779-007-0168-x

    Article  Google Scholar 

  62. Adams S (2005) Electronic non-text material in patent applications—some questions for patent offices, applicants and searchers. World Pat Inf 27(2):99–103

    Article  Google Scholar 

  63. Salton G (1971) The SMART retrieval system—experiments in automatic document processing. Prentice-Hall, Inc, Upper Saddle River

    Google Scholar 

  64. Sparck Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the contribution of our referees, especially Stephen Adams, for many useful suggestions for improving this chapter.

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Authors and Affiliations

  1. Information Retrieval Facility, Techgate, Donau City Strasse 1, Vienna, 1220, Austria

    John I. Tait

  2. European Patent Office, Patentlaan 2, 2288 EE, Rijswijk Zh, Netherlands

    Barou Diallo

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  1. John I. Tait
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  2. Barou Diallo
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Corresponding author

Correspondence to John I. Tait .

Editor information

Editors and Affiliations

  1. Information Retrieval Facility, Donau-City Straße 1, Vienna, 1220, Austria

    Mihai Lupu

  2. Information Retrieval Facility, Donau-City Straße 1, Vienna, 1220, Austria

    Katja Mayer

  3. Information Retrieval Facility, Donau-City Straße 1, Vienna, 1220, Austria

    John Tait

  4. 3LP Advisors, Post Rd. 7003, Dublin, 43016, Ohio, USA

    Anthony J. Trippe

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Tait, J.I., Diallo, B. (2011). Future Patent Search. In: Lupu, M., Mayer, K., Tait, J., Trippe, A. (eds) Current Challenges in Patent Information Retrieval. The Information Retrieval Series, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19231-9_20

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  • DOI: https://doi.org/10.1007/978-3-642-19231-9_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19230-2

  • Online ISBN: 978-3-642-19231-9

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